1. Field of the Invention
The invention relates to computer programs and processes for digital color correction and improving the color rendition of a digitized image. The process enables a user to change or transform an image on a computer monitor via a user's interaction with the system. More specifically, the invention relates to a guided color correction system and process that employs a series of diametrically opposed or contrariwise preview images. The user's response to the guided system determines an appropriate image transformation based on the user's personal preference and perception of the image.
2. Description of the Prior Art
Conventional digital color correction was accomplished using functions available through image editing software applications like Adobe Photoshop. The end result of such correction was accomplished through trial-and-error adjustments made by the user, which was time-consuming and imprecise.
Color correction is the process of manipulating, altering, and fine-tuning color, tone, contrast, saturation, and sharpness to enhance an image's appearance and prepare an image for display or reproduction.
Many factors, like light, can have a negative impact on photography. Sunlight is in constant flux, changing every minute of every day. Early morning and evening sun add orange and yellow to a photograph. The midday sun can wash out colors and increase contrast, and overcast skies add a blue cast to images. Indoor light sources can vary widely, as well, from a neutral Tungsten bulb to a severe green bias caused by fluorescent tubes.
Besides light, hardware devices, such as cameras, scanners, and display devices can also affect image fidelity depending on the device's mechanics, optics, and processing functions. In addition, different photographic films have unique characteristics that capture the same scene in markedly different ways. Output devices, like printers, can also alter the color with varying substrates, and pigments.
Individual perception and personal preference is also a major reason for color correcting images. No two people perceive color in exactly the same way. Physical differences in the way the eye and the brain perceive color signals, and everyone's unique emotional and psychological responses to those stimuli make color correction personal and subjective.
In most instances, the goal of color correction is to remove unwanted color casts to achieve realism, or to improve or match the original image or intent. Color correction is also used to compensate for the limitations and deficiencies of display and output devices, like monitors, desktop printers, or commercial offset printers. In addition, color correction can also be used to create a specific mood or produce unusual, surrealistic visual effects. Virtually every digital image requires some degree of color correction, so it will look its best, no matter what the final presentation may be.
Conventionally, an original image is converted to a digital file and input into a computer through a variety of means, including methods such as scanning, digital still cameras, digital video cameras, or direct art creation using a stylus and graphics tablet. During the input process, images are spatially divided into a matrix of individual segments called picture elements or pixels. These discrete pixels are encoded with an associated set of numeric values that describe the color value with fixed precision accuracy. Digital color correction is accomplished using image editing software that applies mathematical operations or transforms to the numbers that represent each pixel's color value. Applying transformations to these pixels can often result in slightly inaccurate results due to rounding errors. Applying multiple transforms to an image results in multiple rounding errors that can cause visible color artifacts to appear in the image.
The amount of color information contained in each pixel is determined by bit depth. Typically, digital color images contain three color components (RGB) with 8 bits of information per color component (3 to the 8th power). These 24 bit images contain 16.7 million different color possibilities. High-end 48 bit images, which are also currently available, provide billions of colors possibilities.
Color information contained in a digital image is organized within a color space like RGB, (red, green, blue), which is a trichromatic color system (three color components). Computer systems use a color space such as RGB because it encompasses virtually the entire color spectrum in an efficient system. Other color spaces include HSV (hue, saturation, value), HLS (hue, lightness, saturation), CMYK, (cyan, magenta, yellow, black), and LAB (luminace, chromatic component A, chromatic component B).
Red, green, and blue (RGB) are called primary colors or additive colors. When RGB light is added together in 100 percent it produces white. All visible light can be described using combinations of these three colors. Television screens and computer monitors use additive colors to display color images.
Subtractive colors (secondary colors) are cyan, magenta, and yellow (CMY). These are referred to as subtractive because in theory when CMY colors are added together in 100 percent they produce black.
Additive and subtractive colors are exact opposites or inverse of each other. Each additive color has an opposite subtractive color. These opposite color pairs are called complementary colors, and they reside on opposite sides of a color wheel on a shared axis. Because they are inversely related, they have the opposite effect. As an example, increasing one color is the same as decreasing it's complement (i.e. +10% red=−10% cyan). Furthermore, adding any two additive colors makes a subtractive color, and vice versa.
Primary and secondary complementary color pairs are as follows:                Red (R)-Cyan (C)        Green (G)-Magenta (M)        Blue (B)-Yellow (Y)Tertiary colors are created by mixing a primary and a secondary color. A tertiary color also has an opposite complementary color.        
Once the sole domain of highly trained experts, the advent of personal computers and specialized software has made color correction available to a wide audience of often untrained individuals. All of today's popular image editing software products, like Adobe Photoshop, give the user the ability to change the appearance, and color of an image with similar tools and features. These software tools require no prior color correction experience or color theory knowledge.
Until now, digital color correction has been achieved in one of the following five ways: 1.) Color matching. 2.) Automatic processing. 3.) Manual manipulation. 4.) Comparison previews. 5.) Guided color correction.
Color matching is the process of altering the color of a source image by utilizing target values found in reference material. As an example, sky is blue, but with thousands of blue variants, achieving the correct blue is difficult. With color matching, the sky-blue is adjusted to match acceptable pixel values derived from previous artwork or color swatches. U.S. Pat. No. 5,212,546 of Efraim Arazi (1993) discloses the use of reference images in a color correction process to achieve acceptable results, but this system is flawed in a number of significant ways. Because every image is unique and one-of-a-kind, the system's effectiveness is directly limited by the quantity, variety, quality, and preparation of the reference material. As an example, to accurately correct a portrait, this system would require a similar reference image, with matching fleshtone, photographed under the same lighting condition, and reproduced using a duplicate printing process. This system requires experience and knowledge to adjust the source image to match a reference image. Additionally, this system does not provide a means for making adjustments, so the user must rely on conventional systems, which are also flawed. Because of these limitations, this system is rarely used, highly ineffective, and only suitable for general purpose adjustments that provide an overall result, which is far too imprecise for most situations.
Automatic processing functions correct an image based on a set of predefined criteria. U.S. Pat. No. 5,835,627 of Eric Higgins, et al (1998), and U.S. Pat. No. 5,874,988 of Xueming Gu (1999), disclose systems for making automatic color correction. U.S. Pat. No. 5,694,484 of F. Richard Cottrell, et al (1997) disclose an automatic color correction system designed to achieve optimal perceptual quality. Automatic enhancement systems examine and evaluate image data and make assumptions about dynamic range, color, tone, and saturation to achieve a generic or balanced result. More often than not, automatic adjustments fail to meet the user's expectations because most images are not “average”. High-key or low-key images, such as snow scenes or sunsets, and images with a color dominance, like ocean scenes, landscapes, and portraits offer difficult and often insurmountable challenges to automatic processing systems. Whenever subject matter and individual color preference is not considered, the results will be less then optimal.
Manual manipulation is accomplished through the use of numerous on-screen controls, with functions such as curves, levels, color balance, hue and saturation, brightness and contrast, and other tools found in image-editing programs like Adobe Photoshop. Typically, these features permit the user to incrementally increase or decrease individual variables, while an image preview instantly updates changes to assist in the decision making process. The problem with manual manipulation is that it encourages random, haphazard corrections made through trial-and-error. The user continually makes adjustments until a desired result appears. Since guidance is not provided, there is no limitation to the number and kind of edits. As a result, this process is error-prone, and time-consuming. The user can easily become confused, and make mistakes leading to over-processing and the use of destructive, contradictory alterations. This is commonplace since most users have little or no understanding of color theory, or the complexity of color balance.
U.S. Pat. No. 4,941,057 of Donald Lehmbeck, et al (1990), U.S. Pat. No. 5,182,638 of Toshio Tsuboi, et al (1993), and U.S. Pat. No. 5,495,539 of David Sieverding (1996) disclose similar methods of color correction using multiple comparison previews, which are most often arranged in a 3×3 matrix containing nine images. Different parameters and amounts can be compared, evaluated, and selected by the user. Comparison previews are found in many popular image-editing applications including Adobe Photoshop's Variations feature. These methods attempt to simplify color correction, but again, the user is forced to make critical decisions without guidance or knowledge, making the process complex and perplexing. Any incorrect adjustment will create a new problem, so the entire process can easily spiral out of control. As an example, if counterproductive or opposing edits are applied, or if an incorrect amount is used, additional adjustments will be required to compensate for user induced error.
With guided color correction, the outcome is determined by the user's response to a step-by-step process. One such system is a software application called PhotoGenetics, by Q-Research, which is referenced herein as prior art. PhotoGenetics displays two side-by-side images. The image on the left displays the original image. The image on the right displays the source image that has been modified in a particular way. The user rates the quality of the modification using a twenty-two step sliding scale with choices from much better to worse. After the right-hand preview has been rated, the left hand image is updated and a new right-hand adaptation is displayed. This rating process continues until the user reaches a level of satisfaction with the result on the left, and decides to stop the enhancement process. The problem with this system is that the user must evaluate and rate the merits of each modification, making the process ambiguous and confusing. Since there are twenty-two possible responses for each step, this method would likely produce a different result each time it was used. Furthermore, the system does not have a finite number of steps, so the user could terminate the process prematurely, or continue excessively. The system's arbitrary process creates uncertainty and inconsistency, while indicating a lack of consideration for color theory.
With conventional systems, there simply isn't a straightforward, succinct, and unambiguous way for a typical user to achieve accurate color correction results quickly, easily, or proficiently.